In a groundbreaking study in the American Educational Research Journal, University of Delaware Associate Professor Dominique Baker has unveiled significant disparities in how students report extracurricular activities on college applications, highlighting inequities in the admissions process.
Analyzing over 6 million Common App submissions using natural language processing, Baker and her team discovered that white, Asian, wealthier, and private-school students tend to list more activities, leadership roles, and unique accomplishments compared to their peers from underrepresented racial, ethnic, and socioeconomic backgrounds. However, when underrepresented minority students did report leadership roles, they did so at rates comparable to their white and Asian American counterparts.
“All students do not have the ability to sign up for eight, 10 or 15 extracurricular activities,” Baker noted, emphasizing that many students must work to support their families, limiting their participation in extracurriculars.
To address these disparities, Baker recommends reducing the number of activities students can list on applications—suggesting a cap of four or five—to encourage a focus on the quality and intensity of involvement rather than quantity. This approach aims to level the playing field, ensuring that students with limited opportunities can still showcase their potential effectively.
Institutions are beginning to take note; for instance, Lafayette College has recently reduced the number of extracurricular activities it reviews from 10 to six. While data on the impact of such changes is still forthcoming, the move aligns with Baker’s recommendations and signals a shift toward more fair admissions practices.
If you wish to delve deeper into this research and explore its implications for college admissions, Baker is available for interviews and has been in a number of national outlets like The Wall Street Journal, ABC News, and Inside Higher Ed. Her insights could provide valuable perspectives on creating a more fair admissions landscape.
At the University of Delaware, molecular biologist Mona Batish in collaboration with Dr. Robert Akins at Nemours Children Hospital, is studying tiny loops in our cells called circular RNAs — once thought to be useless leftovers, but now believed to play an important role in diseases like cancer and cerebral palsy (CP). This is detailed in a new article in the Journal of Biological Chemistry. What are circular RNAs? They’re a special type of RNA that doesn’t make proteins but instead helps control how genes are turned on and off. Because they’re stable and can be found in blood, they may help doctors detect diseases more easily.
So what’s the connection to cerebral palsy? CP is the most common physical disability in children, but right now it’s diagnosed only after symptoms appear — there’s no clear-cut test for it. Batish and her team are trying to change that.
Working with researchers at Nemours Children’s Health, Batish discovered that in children with CP, a certain circular RNA — circNFIX — is found at much lower levels in muscle cells. This RNA normally helps the body make an important muscle-building protein called MEF2C. When circNFIX is missing or low, MEF2C isn’t made properly, which may lead to the weakened, shorter muscles seen in CP.
This is the first time researchers have shown a link between circular RNAs and human muscle development in cerebral palsy.
Why does this matter? If scientists can confirm this link, it could lead to:
Earlier and more accurate diagnosis of CP using a simple blood test New treatments that help improve muscle development in affected children Batish’s ultimate goal? To create a test that can spot CP at birth — or even before — giving kids a better shot at early treatment and a higher quality of life.
To speak to Batish, contact mediarelations@udel.edu.
Athletes who suffer a concussion have a serious risk of reinjury after returning to play, but identifying which athletes are most vulnerable has always been a bit of a mystery, until now.
Using artificial intelligence (AI), University of Delaware researchers have developed a novel machine learning model that predicts an athlete’s risk of lower-extremity musculoskeletal (MKS) injury after concussion with 95% accuracy. A recent study published in Sports Medicine details the development of the AI model, which builds on previously published research showing that the risk of post-concussion injury doubles, regardless of the sport. The most common post-concussive injuries include sprains, strains, or even broken bones or torn ACLs.
“This is due to brain changes we see post-concussion,” said Thomas Buckley, professor of kinesiology and applied physiology at the College of Health Sciences. These brain changes affect athletes’ balance, cognition, and reaction times and can be difficult to detect in standard clinical testing.
“Even a minuscule difference in balance, reaction time, or cognitive processing of what’s happening around you can make the difference between getting hurt and not,” Buckley said.
How AI is changing injury risk assessment Recognizing the need for enhanced injury reduction risk tools, Buckley collaborated with colleagues in UD’s College of Engineering, Austin Brockmeier, assistant professor of electrical and computer engineering, and César Claros, a fourth-year doctoral student; Wei Qian, associate professor of statistics in the College of Agriculture and Natural Resources; and former KAAP postdoctoral fellow Melissa Anderson, who’s now an assistant professor at Ohio University. To assess injury risk, Brockmeier and Claros developed a comprehensive AI model that analyzes more than 100 variables, including sports and medical histories, concussion type, and pre- and post-concussion cognitive data.
“Every athlete is unique, especially across various sports,” said Brockmeier. “Tracking an athlete’s performance over time, rather than relying on absolute values, helps identify disturbances, deviations, or deficits that, when compared to their baseline, may signal an increased risk of injury.”
While some sports, such as football, carry higher injury risk, the model revealed that individual factors are just as important as the sport played.
“We tested a version of the model that doesn’t have access to the athlete’s sport, and it still accurately predicted injury risk,” Brockmeier said. “This highlights how unique characteristics—not just the inherent risks of a sport—play a critical role in determining the likelihood of future injury,” said Brockmeier.
The research, which tracked athletes over two years, also found that the risk of MSK injury post-concussion extends well into the athlete’s return to play.
“Common sense would suggest that injuries would occur early in an athlete’s return to play, but that’s simply not true,” said Buckley. “Our research shows that the risk of future injury increases over time as athletes compensate and adapt to small deficits they may not even be aware of.”
The next step for Buckey’s Concussion Research Lab is to further collaborate with UD Athletics’ strength and conditioning staff to design real-time interventions that could reduce injury risk.
Beyond sports: AI’s potential in aging research The implications of the UD-developed machine-learning model extend far beyond sports. Brockmeier believes the algorithm could be used to predict fall risk in patients with Parkinson’s disease.
Claros is also exploring how the injury risk reduction model can be applied to aging research with the Delaware Center for Cognitive Aging.
“We want to use brain measurements to investigate whether baseline lifestyle measurements such as weight, BMI, and smoking history are predictive of future mild cognitive impairment or Alzheimer’s disease,” said Claros.
To arrange an interview with Buckley, email UD's media relations team at MediaRelations@udel.edu
